Solved! How to make Google's cloud 20 percent more efficient

NUMA score could be used for better speed, lower costs, and a programmatic method for cloud providers to optimize services

InfoWorld|Mar 12, 2013

In my days as CTO of a technology company, I often received outside advice as to how I could improve my technology. A new set of eyes is a good thing, and often the recommendations were solid ideas I implemented to improve the product.

It appears that some cloud providers, such as Google, are also open to suggestions, as Phys.org reports: "Computer scientists at the University of California at San Diego, and Google have developed a novel approach that allows the massive infrastructure powering cloud computing to run more efficiently. The new approach can make these warehouse-scale computers run as much as 15 to 20 percent more efficiently."

The scholars analyzed a bunch of Google Web services, including Gmail and search, and performed two basic steps:

Gathered live data from the Google cloud in real time

Analyzed that data on an isolated server

Based on the results of these experiments, the scientists developed a metric, called the NUMA score, that can determine how well random-access memory is allocated in large-scale computers. Optimizing the NUMA score may lead to 15 to 20 percent improvements in efficiency around processing in the cloud computing platform.

I suspect many public cloud computing providers need to optimize their systems. With that optimization should come more efficiency, which should lead to the ability to handle more tenants and, thus, the chance to increase revenue for the cloud providers.

Cloud users should also see this benefit in faster systems, as well as lower operational costs. Moreover, such metrics should let cloud users monitor and adjust tunable parameters for their specific type of processing they're doing. However, I doubt Google would let you do that for Gmail and search.

Such optimization analysis could be a new area of discipline as cloud providers continue to manage thousands of servers. After all, it pays to have heavy thinking occur around how well those systems are functioning. A small improvement could save millions of dollars a year.